Module 11
NIST/SEMATECH e-Handbook of Statistical Methods

An ordered sequence of values of a variable at equally spaced time intervals.
From fpp3:
Examples of time series data include:
Annual Google profits
Quarterly sales results for Amazon
Monthly rainfall
Weekly retail sales
Daily IBM stock prices
Hourly electricity demand
5-minute freeway traffic counts
Time-stamped stock transaction data
Anything that is observed sequentially over time is a time series.
The mean cost is: 10.
The SSE is: 36.
The MSE is: 3.
pc_ebt <- read_table(
"Year Earnings_M Mean Error Squared_Error
1985 46.163 48.676 -2.513 6.313
1986 46.998 48.676 -1.678 2.814
1987 47.816 48.676 -0.860 0.739
1988 48.311 48.676 -0.365 0.133
1989 48.758 48.676 0.082 0.007
1990 49.164 48.676 0.488 0.239
1991 49.548 48.676 0.872 0.761
1992 48.915 48.676 0.239 0.057
1993 50.315 48.676 1.639 2.688
1994 50.768 48.676 2.092 4.378", col_names = TRUE
)
pc_ebtThe mean Earnings ($M) is: 48.68.
The SSE is: 18.129.
The MSE is: 1.81.
Global Silicon Wafer Shipments
Units are in “million square inches (MSI)”
| year | quarter | msi |
|---|---|---|
| 2001 | Q1 | 1250 |
| 2001 | Q2 | 988 |
| … | … | … |
msi_ts <- msi_historical |>
pivot_longer(cols = -year, names_to = "quarter", values_to = "quarter_value", ) |>
separate_wider_delim(cols = quarter_value, names = c("quarter_2", "msi"), delim = " ") |>
mutate(msi = as.numeric(str_remove(msi, ","))) |>
select(-quarter_2) |>
mutate(year_quarter = str_c(year, " ", quarter)) |>
select(year_quarter, msi) |>
mutate(year_quarter = yearquarter(year_quarter)) |>
as_tsibble(index = year_quarter)
msi_tsApplied Statistical Techniques